Department of Computer Science & Engineering

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Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

Last decade has witnessed rapid growth for the popularity of Convolutional Neural Networks (CNNs), in detecting and classifying objects. The self trainable nature of CNNs makes them the strongest candidate as a classifier and a feature extractor. However, many of the existing CNN architectures fail recognizing texts or objects under input rotation and scaling. This paper introduces an elegant approach, 'Scale and Rotation Corrected CNN (SRC-CNN)' for scale and rotation invariant text recognition, exploiting the concept of principal component of characters. Prior to training and testing with baseline CNN, 'SRC-CNN' maps each character image to a reference orientation and scale, which is again derived from the character image itself. SRC-CNN is capable of recognizing characters in a document, even though they differ in orientation and scale greatly. The proposed method does not demand any training with samples which are scaled or rotated. The performance of proposed approach is validated on different character data sets like MNIST, MNIST_rot_12k and English alphabets and compared with state of the art rotation invariant classification networks. SRC-CNN is a generalized approach and can be extended for rotation and scale invariant classification of many other datasets as well, choosing any appropriate baseline CNN. Here we have demonstrated the generality of the proposed SRC-CNN on MNIST Fashion data set and found to perform well in rotation and scale invariant classification of objects as well. This paper demonstrates how the basic PCA based rotation and scale invariant image recognition can be integrated to CNN for achieving better rotational and scale invariances in classification. © 2018 ACM.

World University root node

ACM International Conference Proceeding Series

Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and Rotation Invariant Character Recognition: SRC-CNN for Scale and Rotation Invariant Character Recognition

Due to high variability in writing style of different individuals, non-centered and non-uniformly scaled optical characters are very difficult to recognize. Several techniques are proposed in-order to solve the recognition problem. In this work, we highlight that the performance of optical character classifiers which are based on the deep learning framework can be improved through feature-map alignment. Here, we have used spatial transformer network to align the feature maps of a convolutional neural network model which is proposed for the classification problem. We demonstrate that with the proposed framework not only the slight transformed versions which are usually considered in the conventional datasets can be classified with high accuracy, but also highly non-uniform in scale characters can also be fairly recognized with quite higher accuracy. We evaluate our proposed model on CMATERdb 3.1.3 database which consists of isolated Bangla handwritten compound characters and our model obtained 97.86 % recognition accuracy in the original database and 96.34 % on various rotated data in training and testing. © 2017 IEEE.

2017 9th International Conference on Advances in Pattern Recognition, ICAPR 2017

Improving Isolated Bangla Compound Character Recognition Through Feature-map Alignment

The science of uniquely identifying a person based on his or her physiological or behavioral characteristics is termed biometrics. Physiological characteristics include face, iris, fingerprint, and DNA, whereas behavioral modalities include handwriting, gait, and {\ldots} Biometric authentication systems have widespread applications in national identification programs, banking, attendance system, and surveillance. Recent development and advances in technology has further led to the automation of these systems. Automated {\ldots}

Deep Learning in Biometrics

Deep Learning: Fundamentals and Beyond

IIIT-Delhi

Context-aware face recognition

In the last few years, parameterized complexity has emerged as a new tool to analyze the running time of randomized local search algorithm. However, such analysis are few and far between. In this paper, we do a parameterized running time analysis of a randomized local search algorithm and a (1+1) EA for a classical graph partitioning problem, namely, Max l-Uncut, and its balanced counterpart Max Balanced l-Uncut. In Max l-Uncut, given an undirected graph G = (V, E), the objective is to find a partition of V(G) into l parts such that the number of uncut edges - edges within the parts - is maximized. In the last few years, Max l-Uncut and Max Balanced l-Uncut are studied extensively from the approximation point of view. In this paper, we analyze the parameterized running time of a randomized local search algorithm (RLS) for Max Balanced l-Uncut where the parameter is the number of uncut edges. RLS generates a solution of specific fitness in polynomial time for this problem. Furthermore, we design a fixed parameter tractable randomized local search and a (1 + 1) EA for Max l-Uncut and prove that they perform equally well. © 2018 Copyright held by the owner/author(s).

GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

A parameterized runtime analysis of randomized local search and evolutionary algorithm for max l-uncut

In this paper, we design and implement a human–machine interaction application, which enables a visually challenged person to locate and manipulate personal objects in her/his neighborhood. In this setting, we need to develop a tool (embedded in a mobile phone) which is capable of sensing, computing, and guiding the human arm toward the object. This involves solving the following two subproblems: (1) recognition of objects in the input images, and (2) generating control signals, to guide the human for navigation, to reach the desired destination. For the former subproblem, we adapt the bag-of-words framework for recognition and matching on mobile phones. For the latter subproblem, we have developed a moment-based human servoing algorithm which is able to generate commands that help the visually impaired human to localize his hand with respect to the object of interest. All necessary computations take place on the mobile phone. The proposed object recognition and vision-based control design are deployed on a low-/mid-end mobile phone. This can lead to a wide range of applications. With our proposed design and implementation, we demonstrate that our application is effective and accurate, with a high reliability of convergence for different experimental settings. © 2018, King Fahd University of Petroleum & Minerals.

Arabian Journal for Science and Engineering

Guide Me: Recognition and Servoing on Mobiles

We forget action steps and perform some unwanted action movements as amateur performers during our daily exercise routine, dance performances, etc. To improve our proficiency, it is important that we get a feedback on our performances in terms of where we went wrong. In this paper, we propose a framework for analyzing and issuing reports of action segments that were missed or anomalously performed. This involves comparing the performed sequence with the standard action sequence and notifying when misalignments occur. We propose an exemplar based Approximate String Matching (ASM) technique for detecting such anomalous and missing segments in action sequences. We compare the results with those obtained from the conventional Dynamic Time Warping (DTW) algorithm for sequence alignment. It is seen that the alignment of the action sequences under conventional DTW fails in the presence of missed action segments and anomalous segments due to its boundary condition constraints. The performance of the two techniques has been tested on a complex aperiodic human action dataset with Warm up exercise sequences that we developed from correct and incorrect executions by multiple people. The proposed ASM technique shows promising alignment and missed/anomalous notification results over this dataset. © Springer Nature Singapore Pte Ltd. 2018.

Communications in Computer and Information Science

Detecting missed and anomalous action segments using approximate string matching algorithm

Smart interconnected vehicles generate a huge amount of data to be used by a wide range of applications. Although cloud based data management is currently in practice, for many applications serving road safety or traffic regulation, it is utmost important that applications access these data at the site itself for improved quality of service. Road side units (RSUs) play a crucial role in handling these vast amount of vehicular data and serving the running applications in turn. In this current era of edge computing, in-place data access is also proven to be advantageous from cost point of view. As multiple applications from different service providers are interested to access different fragments of these data, a robust access control mechanism is needed to ensure desired level of security as well as reliability for these data. In this paper, we introduce B2VDM, a novel architecture for vehicular data management at RSUs, that provides a seamless access control using Blockchain technology. The proposed B2VDM framework also implements a simple load distribution module, which maintains the reliability by minimizing the number of packet drops at a heavily loaded RSU during peak hours. An extensive evaluation using Etherium Blockchain validates the effectiveness of the proposed architecture. © 2018 IEEE.

2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018

B2VDM: Blockchain Based Vehicular Data Management

In this paper, we present an identity-based encryption scheme from codes with efficient key revocation. Recently, in Crypto 2017, Gaborit et al. proposed a first identity-based encryption scheme from codes with rank metric, called RankIBE. To extract the decryption key from any public identity, they constructed a trapdoor function which relies on RankSign, a signature scheme proposed by Gaborit et al. in PQCrypto 2014. We adopt the same trapdoor function to add efficient key revocation functionality in the RankIBE scheme. Our revocable IBE scheme from codes with rank metric makes use of a binary tree data structure to reduce the amount of work in terms of key updates for the key authority. The total size of key updates requires logarithmic complexity in the maximum number of users and linear in the number of revoked users. We prove that our revocable IBE scheme is selective-ID secure in the random oracle model, under the hardness of three problems: the Rank Syndrome Decoding (RSD) problem, the Augmented Low Rank Parity Check Code (LRPC+) problem, and the Rank Support Learning (RSL) problem. © Springer International Publishing AG, part of Springer Nature 2018.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Revocable identity-based encryption from codes with rank metric

The task of automatic image annotation attempts to predict a set of semantic labels for an image. Majority of the existing methods discover a common latent space that combines content and semantic image similarity using the metric learning kind of global learning framework. This limits their applicability to large datasets. On the other hand, there are few methods which entirely focus on learning a local latent space for every test image. However, they completely ignore the global structure of the data. In this work, we propose a novel image annotation method which attempts to combine best of both local and global learning methods. We introduce the notion of neighborhood-types based on the hypothesis that similar images in content/feature space should also have overlapping neighborhoods. We also use graph diffusion as a mechanism for label transfer. Experiments on publicly available datasets show promising performance. © Springer Nature Singapore Pte Ltd. 2018.